Wang, Jane X.

Reinforcement learning, fast and slow

Botvinick, M., Ritter, S., Wang, J. X., Kurth-Nelson, Z., Blundell, C., & Hassabis, D. (2019) · Trends in Cognitive Sciences

Reinforcement Learning in RoboticsNeural and Behavioral Psychology Studies

Cited by 667

Prefrontal cortex as a meta-reinforcement learning system

Wang, J. X., Kurth-Nelson, Z., Kumaran, D., Tirumala, D., Soyer, H., Leibo, J. Z., Hassabis, D., & Botvinick, M. (2018) · Nature Neuroscience

Neurotransmitter Receptor Influence on BehaviorNeural and Behavioral Psychology Studies

Cited by 596

Learning to reinforcement learn

Wang, J. X., Kurth-Nelson, Z., Tirumala, D., Soyer, H., Leibo, J. Z., Munos, R., Blundell, C., Kumaran, D., & Botvinick, M. (2016) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 579

Deep reinforcement learning and its neuroscientific implications

Botvinick, M., Wang, J. X., Dabney, W., Miller, K. J., & Kurth-Nelson, Z. (2020) · Neuron

Reinforcement Learning in RoboticsNeural and Behavioral Psychology Studies

Cited by 269

Meta-learning in natural and artificial intelligence

Wang, J. X. (2021) · Current Opinion in Behavioral Sciences

Domain Adaptation and Few-Shot LearningMemory and Neural Mechanisms

Cited by 171

Can language models learn from explanations in context?

Lampinen, A. K., Dasgupta, I., Chan, S. C. Y., Matthewson, K., Tessler, M. H., Creswell, A., McClelland, J. L., Wang, J. X., & Hill, F. (2022) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 163

Covert rapid action-memory simulation (CRAMS): a hypothesis of hippocampal-prefrontal interactions for adaptive behavior

Wang, J. X., Cohen, N. J., & Voss, J. L. (2015) · Neurobiology of Learning and Memory

Memory and Neural MechanismsNeural and Behavioral Psychology Studies

Cited by 75

Causal Reasoning from Meta-reinforcement Learning

Dasgupta, I., Wang, J., Chiappa, S., Mitrovic, J., Ortega, P., Raposo, D., Hughes, E., Battaglia, P., Botvinick, M., & Kurth-Nelson, Z. (2019) · arXiv

Reinforcement Learning in RoboticsBayesian Modeling and Causal Inference

Cited by 71

Meta-learning of Sequential Strategies

Ortega, P. A., Wang, J. X., Rowland, M., Genewein, T., Kurth-Nelson, Z., Pascanu, R., Heess, N., Veness, J., Pritzel, A., Sprechmann, P., et al. (2019) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 47

Meta-learned models of cognition

Binz, M., Dasgupta, I., Jagadish, A. K., Botvinick, M., Wang, J. X., & Schulz, E. (2024) · Behavioral and Brain Sciences

Cognitive Science and Education ResearchBayesian Modeling and Causal Inference

Cited by 38

CogBench: a large language model walks into a psychology lab

Coda-Forno, J., Binz, M., Wang, J. X., & Schulz, E. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 15

Tell me why! Explanations support learning relational and causal structure

Lampinen, A. K., Roy, N. A., Dasgupta, I., Chan, S. C. Y., Tam, A. C., McClelland, J. L., Yan, C., Santoro, A., Rabinowitz, N. C., Wang, J. X., et al. (2021) · arXiv

Reinforcement Learning in RoboticsExplainable Artificial Intelligence (XAI)

Cited by 14

Alchemy: A benchmark and analysis toolkit for meta-reinforcement learning agents

Wang, J. X., King, M., Porcel, N., Kurth-Nelson, Z., Zhu, T., Deck, C., Choy, P., Cassin, M., Reynolds, M., Song, F., et al. (2021) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 10

Scaling Instructable Agents Across Many Simulated Worlds

SIMA Team, Raad, M. A., Ahuja, A., Barros, C., Besse, F., Bolt, A., Bolton, A., Brownfield, B., Buttimore, G., Cant, M., et al. (2024) · arXiv

Reinforcement Learning in RoboticsAI-based Problem Solving and Planning

Cited by 9